A systematic extreme learning machine approach to analyze visitors' thermal comfort at a public urban space

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@Article{Kariminia:2016:RSER,
  author =       "Shahab Kariminia and Shahaboddin Shamshirband and 
                 Shervin Motamedi and Roslan Hashim and 
                 Chandrabhushan Roy",
  title =        "A systematic extreme learning machine approach to
                 analyze visitors' thermal comfort at a public urban
                 space",
  journal =      "Renewable and Sustainable Energy Reviews",
  volume =       "58",
  pages =        "751--760",
  year =         "2016",
  ISSN =         "1364-0321",
  DOI =          "doi:10.1016/j.rser.2015.12.321",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1364032115017049",
  abstract =     "Thermal quality of open public spaces in every city
                 influences its residents' outdoor life. Higher level of
                 thermal comfort attracts more visitors to such places;
                 hence, brings benefits to the community. Previous
                 research works have used the body energy balance or
                 adaptation model for predicting the thermal comfort in
                 outdoor spaces. However, limited research works have
                 applied computational methods in this field. For the
                 first of its' type, this study applied a systematic
                 approach using a class of soft-computing methodology
                 known as the extreme learning machine (ELM) to forecast
                 the thermal comfort of the subject visitors at an open
                 area in Iran. For data collection, this study used
                 common thermal indices for assessing the thermal
                 perceptions of the subjects. The fieldworks comprised
                 of measuring the micro-climatic conditions and
                 interviewing the visitors. This study compared the
                 results of ELM with other conventional soft-computing
                 methods (i.e., artificial neural network (ANN) and
                 genetic programming (GP)). The findings indicate that
                 the ELM results match with the field data. This implies
                 that a model constructed by ELM can accurately predict
                 visitors' thermal sensations. We conclude that the
                 proposed model's predictability performance is reliable
                 and superior compared to other approaches (i.e., GP and
                 ANN). Besides, the ELM methodology significantly
                 reduces training time for a Neural Network as compared
                 to the conventional methods.",
  keywords =     "genetic algorithms, genetic programming, Outdoor
                 thermal comfort, Open urban area, Extreme learning
                 machine, Regression, Moderate climate, Dry climate",
}

Genetic Programming entries for Shahab Kariminia Shahaboddin Shamshirband Shervin Motamedi Roslan Hashim Chandrabhushan Roy

Citations